Notebook: Modélisation GAMMs de la diphtongue descendante /ai/ en mandarin standard
Chenyu LI
Ce notebook est une analyse acoustique des diphtongues descendantes en mandarin standard, en appliquant la modélisation GAMMs (Generated Additive Mixed Models).
#install.packages('rmarkdown')# Importation des emballages
#install.packages("itsadug")
library(nlme)
library(ggplot2)
library(mgcv)
library(itsadug)
source("gamm_hacks.r")
#install.packages("tidyverse")
library(tidyverse)Après l’importation des emballages, nous lisons les bases de données en format .tsv.
# Importation des données
au <- read.table(file="au0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ai <- read.table(file="ai0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ei <- read.table(file="ei0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ou <- read.table(file="ou0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)Changement de nature des variables des bases de données.
Le critère du changement est que toutes les variables numériques sont numérées et toutes les chaînes de caractère sont factorées.
/ai/
La première diphtongue à analyser est /ai/.
ai$sexe<-as.factor(ai$sexe)
ai$ton<-as.factor(ai$ton)
ai$pow<-as.factor(ai$pow)
ai$contexte.D<-as.factor(ai$contexte.D)
ai$contexte.G<-as.factor(ai$contexte.G)
ai$f1<-as.numeric(ai$f1)NAs introduced by coercionai$f2<-as.numeric(ai$f2)NAs introduced by coercionai$f3<-as.numeric(ai$f3)NAs introduced by coercionai$f0<-as.numeric(ai$f0)NAs introduced by coercionNous voyons la tête du fichier de /ai/.
head(ai)Dans la base de données, nous pouvons remarquer les variables basiques dans les colonnes numero, sexe, locuteur, diphtongue ; les variables catégorielles comme ton, pow (position dans le mot), contexte.G et contexte.D (les consonnes précédentes et suivantes) ; et les variables numériques comme duree.ms, f0, f1, f2 et f3. Chaque diphtongue est mesurée par 11 points temporels à intervalle égale (0%, 10%, 20%, …, 100%).
# Regroupement par les sexes
ai.mas <- droplevels(subset(ai,sexe=="M"))
ai.fem <- droplevels(subset(ai,sexe=="F"))Masculin
Nous allons voir les données des locuteurs.
F1
Avant de lancer les modèles, nous voyons les trajectoires de f1 avec les tons différents, avec la considération des sexes et de la durée.
ggplot(ai.mas, aes(x=measurement.no, y=f1, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()
Modèles de base
Le premier modèle est avec un smooth basic de ton 1 et de smooths de différence.
ai.mas$ton.ord <- as.ordered(ai.mas$ton)
contrasts(ai.mas$ton.ord) <- "contr.treatment"
ai.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 508.92 20.76 24.514 < 2e-16 ***
ton.ord2 120.43 22.33 5.393 8.47e-08 ***
ton.ord3 173.89 23.07 7.536 1.01e-13 ***
ton.ord4 109.93 21.51 5.110 3.81e-07 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.434 5.392 5.263 5.67e-05 ***
s(measurement.no):ton.ord2 1.017 1.032 10.413 0.00111 **
s(measurement.no):ton.ord3 1.001 1.002 4.728 0.02980 *
s(measurement.no):ton.ord4 3.570 4.385 7.968 1.47e-06 ***Ensuite les plots des prédictions et de smooth de différence entre les tons.
plot_smooth(ai.mas.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 2.020202
4.040404 - 7.070707
plot_diff(ai.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
2.323232 - 10.000000
plot_diff(ai.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 2.929293
6.969697 - 10.000000
Analyse : Selon les images, les trajactoires de f1 dans les 4 tons peuvent généralement être différentes significativement, à l’excepetion de celles des ton 3 et 4, qui ne sont pas différentes significativement dans la portion centrale.
Maintenant nous tenons compte de l’influence de duree.ms sur les trajactoires.
ai.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 521.48 20.47 25.471 < 2e-16 ***
ton.ord2 96.33 22.45 4.291 1.94e-05 ***
ton.ord3 174.32 22.42 7.777 1.74e-14 ***
ton.ord4 97.59 21.32 4.578 5.25e-06 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.526 5.458 2.689 0.0153 *
s(duree.ms.) 5.581 6.507 5.202 3.61e-05 ***
ti(measurement.no,duree.ms.) 7.258 9.082 4.050 3.73e-05 ***
s(measurement.no):ton.ord2 2.838 3.498 7.788 1.66e-05 ***
s(measurement.no):ton.ord3 2.279 2.815 3.364 0.0173 *
s(measurement.no):ton.ord4 4.009 4.879 6.286 1.52e-05 ***Les plots avec la considération de la durée.
plot_smooth(ai.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.mas.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.mas$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
The model with regard of f0.
ai.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 515.84 19.38 26.619 < 2e-16 ***
ton.ord2 98.96 21.39 4.628 4.21e-06 ***
ton.ord3 133.59 22.60 5.912 4.69e-09 ***
ton.ord4 99.55 19.87 5.009 6.49e-07 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.043 6.028 3.347 0.002781 **
s(f0) 2.675 3.219 6.283 0.000272 ***
ti(measurement.no,f0) 3.877 4.994 2.958 0.011798 *
s(measurement.no):ton.ord2 3.114 3.839 7.162 2.42e-05 ***
s(measurement.no):ton.ord3 2.262 2.803 6.325 0.000460 ***
s(measurement.no):ton.ord4 4.213 5.118 8.474 < 2e-16 ***The plot of such model.
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.mas.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
extra models
# random intercepts only
ai.mas.gam.int <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr") +
s(numero, bs="re"),
data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.int)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 456.59 28.69 15.914 < 2e-16 ***
ton.ord2 102.72 21.32 4.817 1.69e-06 ***
ton.ord3 134.32 22.50 5.971 3.32e-09 ***
ton.ord4 106.18 19.91 5.333 1.20e-07 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.1635 6.149 3.407 0.002221 **
s(f0) 2.7581 3.306 6.210 0.000251 ***
ti(measurement.no,f0) 4.2159 5.440 2.916 0.011193 *
s(measurement.no):ton.ord2 3.2824 4.041 7.045 1.4e-05 ***
s(measurement.no):ton.ord3 2.5371 3.148 6.155 0.000312 ***
s(measurement.no):ton.ord4 4.3255 5.243 8.514 < 2e-16 ***
s(numero) 0.8844 1.000 7.653 0.003254 ** plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
fvisgam(ai.mas.gam.int, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
data length [31] is not a sub-multiple or multiple of the number of rows [30]
# random intercepts + slopes
ai.mas.gam.slope <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr") +
s(numero, bs="re") +
s(numero, measurement.no, bs="re"),
data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.slope)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 455.37 26.73 17.034 < 2e-16 ***
ton.ord2 103.53 21.30 4.861 1.37e-06 ***
ton.ord3 134.92 22.47 6.005 2.71e-09 ***
ton.ord4 106.34 19.85 5.356 1.06e-07 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.1531792 6.138 4.177 0.000328 ***
s(f0) 2.7569210 3.305 6.089 0.000298 ***
ti(measurement.no,f0) 4.1973202 5.423 2.853 0.012838 *
s(measurement.no):ton.ord2 3.2594077 4.013 6.791 2.16e-05 ***
s(measurement.no):ton.ord3 2.5316088 3.141 6.168 0.000308 ***
s(measurement.no):ton.ord4 4.3419115 5.262 8.353 < 2e-16 ***
s(numero) 0.0002281 1.000 0.000 0.606058
s(numero,measurement.no) 0.9128615 1.000 10.485 0.000716 ***plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
fvisgam(ai.mas.gam.slope, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
data length [31] is not a sub-multiple or multiple of the number of rows [30]
plot_smooth(ai.mas.gam.slope, view="measurement.no", plot_all="ton.ord",
rug=F, rm.ranef=T)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 137.402261031236.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
#plot_diff(ai.mas.gam.slope, view="measurement.no",
#comp=list(ton.ord = c("2","4")), rm.ranef=T)#ai.mas.gam.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
# s(f0, bs="cr") +
# ti(measurement.no, f0) +
# s(measurement.no, by=ton.ord, bs="cr") +
# s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
# data=ai.mas, method="fREML")
#summary.coefs(ai.mas.gam.smooth)ai.mas$start.event <- ai.mas$measurement.no==0
r1 <- start_value_rho(ai.mas.gam.f0)
ai.mas.gam.AR <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="fREML",
rho=r1, AR.start=ai.mas$start.event)
summary.coefs(ai.mas.gam.AR)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 528.66 28.03 18.858 < 2e-16 ***
ton.ord2 91.67 30.74 2.982 0.002937 **
ton.ord3 115.66 32.36 3.574 0.000369 ***
ton.ord4 85.10 28.73 2.961 0.003137 **
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 6.036 7.043 3.644 0.000664 ***
s(f0) 1.878 2.303 2.973 0.056214 .
ti(measurement.no,f0) 4.238 5.488 2.497 0.025727 *
s(measurement.no):ton.ord2 3.946 4.932 6.458 9.21e-06 ***
s(measurement.no):ton.ord3 3.172 4.024 4.928 0.000613 ***
s(measurement.no):ton.ord4 5.015 6.080 7.806 < 2e-16 ***plot_smooth(ai.mas.gam.AR, view="measurement.no", plot_all="ton.ord",
rug=F, rm.ranef=T)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 137.402261031236.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.mas.gam.AR, view="measurement.no",
comp=list(ton.ord = c("2","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 137.402261031236.
measurement.no window(s) of significant difference(s):
0.000000 - 1.515152
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.mas.gam.AR, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
Central portion
We now focus on the central portion of the diphthongs, which means the portions without the inluence of the consonant contexts.
ai.central<-droplevels(subset(ai,measurement.no>=2))
ai.central<-droplevels(subset(ai.central,measurement.no<=8))
ai.central.mas <- droplevels(subset(ai.central,sexe=="M"))
ai.central.fem <- droplevels(subset(ai.central,sexe=="F"))ai.central.mas$ton.ord <- as.ordered(ai.central.mas$ton)
contrasts(ai.central.mas$ton.ord) <- "contr.treatment"
ai.central.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 472.08 19.51 24.201 < 2e-16 ***
ton.ord2 171.54 20.96 8.184 1.28e-15 ***
ton.ord3 225.45 21.68 10.399 < 2e-16 ***
ton.ord4 187.63 20.19 9.292 < 2e-16 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 2.404 2.924 9.433 1.01e-05 ***
s(measurement.no):ton.ord2 1.002 1.004 0.147 0.7039
s(measurement.no):ton.ord3 1.002 1.005 1.309 0.2530
s(measurement.no):ton.ord4 2.529 3.067 2.551 0.0533 . plot_smooth(ai.central.mas.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 8.000000
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.848485 - 8.000000
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 3.030303
5.818182 - 8.000000
ai.central.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 483.05 19.43 24.864 < 2e-16 ***
ton.ord2 153.92 21.26 7.239 1.19e-12 ***
ton.ord3 225.56 21.26 10.609 < 2e-16 ***
ton.ord4 175.66 20.20 8.697 < 2e-16 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 2.393 2.910 10.314 3.87e-06 ***
s(duree.ms.) 4.484 4.852 4.597 0.000297 ***
ti(measurement.no,duree.ms.) 1.236 1.431 5.669 0.006689 **
s(measurement.no):ton.ord2 1.000 1.000 0.094 0.759550
s(measurement.no):ton.ord3 1.000 1.001 1.487 0.223105
s(measurement.no):ton.ord4 2.587 3.131 2.965 0.029587 * plot_smooth(ai.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.mas.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.central.mas$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
ai.central.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 480.55 18.43 26.068 < 2e-16 ***
ton.ord2 159.40 20.21 7.886 1.3e-14 ***
ton.ord3 182.35 21.24 8.583 < 2e-16 ***
ton.ord4 178.19 18.85 9.455 < 2e-16 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 2.449 2.975 7.785 5.8e-05 ***
s(f0) 2.888 3.362 5.997 0.000297 ***
ti(measurement.no,f0) 1.005 1.010 6.139 0.013357 *
s(measurement.no):ton.ord2 1.000 1.000 0.233 0.629530
s(measurement.no):ton.ord3 1.000 1.001 0.006 0.938828
s(measurement.no):ton.ord4 2.698 3.256 2.878 0.031657 * plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 100.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 120.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 140.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.mas.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.central.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; with 30 values ranging from 99.155553 to 204.798228.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
F2
Basic models
Now we look at the trajectories of f2 in different tones with regard of the sexes and the durations.
ggplot(ai.mas, aes(x=measurement.no, y=f2, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()
Then we fit the same model with a basic smooth of tone 1 and difference smooths.
ai.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1808.78 25.76 70.213 < 2e-16 ***
ton.ord2 -190.78 27.71 -6.885 9.73e-12 ***
ton.ord3 -182.21 28.63 -6.364 2.89e-10 ***
ton.ord4 -157.18 26.70 -5.887 5.22e-09 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 3.783 4.654 6.929 7.58e-06 ***
s(measurement.no):ton.ord2 1.000 1.001 0.783 0.376
s(measurement.no):ton.ord3 1.001 1.001 0.225 0.635
s(measurement.no):ton.ord4 1.004 1.008 0.360 0.550 Now the plots of f2 with different tones.
plot_smooth(ai.mas.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 10.000000
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 2.020202
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
5.252525 - 10.000000
The model that accounts for the influence of duree.ms. on the trajectories.
ai.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1826.36 25.30 72.182 < 2e-16 ***
ton.ord2 -210.79 27.64 -7.627 5.22e-14 ***
ton.ord3 -190.56 27.77 -6.861 1.15e-11 ***
ton.ord4 -178.75 26.32 -6.793 1.81e-11 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 3.893 4.783 8.213 6.26e-07 ***
s(duree.ms.) 3.903 4.707 5.118 0.000252 ***
ti(measurement.no,duree.ms.) 2.869 4.012 12.552 < 2e-16 ***
s(measurement.no):ton.ord2 1.001 1.002 1.332 0.248618
s(measurement.no):ton.ord3 1.001 1.003 0.351 0.553691
s(measurement.no):ton.ord4 1.001 1.003 0.038 0.847241 The plots with regard the durations.
plot_smooth(ai.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.mas$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
ai.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1809.15 24.68 73.316 < 2e-16 ***
ton.ord2 -187.20 27.28 -6.862 1.21e-11 ***
ton.ord3 -201.12 28.74 -6.999 4.80e-12 ***
ton.ord4 -156.85 25.33 -6.191 8.79e-10 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.100 5.036 9.733 < 2e-16 ***
s(f0) 5.833 6.673 5.023 2.82e-05 ***
ti(measurement.no,f0) 2.307 2.805 2.708 0.0422 *
s(measurement.no):ton.ord2 1.001 1.002 0.005 0.9472
s(measurement.no):ton.ord3 1.001 1.001 0.034 0.8553
s(measurement.no):ton.ord4 1.004 1.008 0.011 0.9373 plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.mas.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
extra models
# random intercepts only
ai.mas.gam.f2.int <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr") +
s(numero, bs="re"),
data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.f2.int)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1809.17 24.68 73.315 < 2e-16 ***
ton.ord2 -187.22 27.28 -6.863 1.20e-11 ***
ton.ord3 -201.16 28.74 -7.000 4.76e-12 ***
ton.ord4 -156.88 25.33 -6.192 8.74e-10 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.127e+00 5.067 9.697 < 2e-16 ***
s(f0) 5.824e+00 6.663 5.023 2.86e-05 ***
ti(measurement.no,f0) 2.327e+00 2.827 2.705 0.0422 *
s(measurement.no):ton.ord2 1.000e+00 1.000 0.005 0.9413
s(measurement.no):ton.ord3 1.000e+00 1.000 0.034 0.8542
s(measurement.no):ton.ord4 1.000e+00 1.000 0.011 0.9171
s(numero) 2.024e-05 1.000 0.000 0.6922 plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
fvisgam(ai.mas.gam.f2.int, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
data length [31] is not a sub-multiple or multiple of the number of rows [30]
# random intercepts + slopes
ai.mas.gam.f2.slope <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr") +
s(numero, bs="re") +
s(numero, measurement.no, bs="re"),
data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.f2.slope)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1783.01 31.63 56.364 < 2e-16 ***
ton.ord2 -185.70 27.28 -6.806 1.75e-11 ***
ton.ord3 -200.83 28.72 -6.994 4.98e-12 ***
ton.ord4 -154.22 25.39 -6.073 1.80e-09 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.124e+00 5.064 7.800 4.40e-07 ***
s(f0) 5.768e+00 6.604 4.719 6.49e-05 ***
ti(measurement.no,f0) 2.322e+00 2.822 2.658 0.0451 *
s(measurement.no):ton.ord2 1.000e+00 1.000 0.022 0.8812
s(measurement.no):ton.ord3 1.000e+00 1.000 0.024 0.8774
s(measurement.no):ton.ord4 1.000e+00 1.001 0.033 0.8568
s(numero) 3.609e-05 1.000 0.000 0.4983
s(numero,measurement.no) 6.351e-01 1.000 1.741 0.0975 . plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
fvisgam(ai.mas.gam.f2.slope, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
data length [31] is not a sub-multiple or multiple of the number of rows [30]
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", plot_all="ton.ord",
rug=F, rm.ranef=T)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 137.402261031236.
* numero : numeric predictor; set to the value(s): 164. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
#plot_diff(ai.mas.gam.slope, view="measurement.no",
#comp=list(ton.ord = c("2","4")), rm.ranef=T)#ai.mas.gam.f2.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
# s(f0, bs="cr") +
# ti(measurement.no, f0) +
# s(measurement.no, by=ton.ord, bs="cr") +
# s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
# data=ai.mas, method="fREML")
#summary.coefs(ai.mas.gam.f2.smooth)Central portion
The central portion:
ai.central.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1797.78 28.10 63.974 < 2e-16 ***
ton.ord2 -169.65 30.20 -5.618 2.77e-08 ***
ton.ord3 -154.65 31.23 -4.952 9.20e-07 ***
ton.ord4 -136.24 29.09 -4.683 3.38e-06 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 1.005 1.011 7.229 0.00732 **
s(measurement.no):ton.ord2 1.001 1.002 0.268 0.60543
s(measurement.no):ton.ord3 1.001 1.002 0.035 0.85460
s(measurement.no):ton.ord4 1.001 1.003 0.595 0.44147 plot_smooth(ai.central.mas.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 8.000000
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
Difference is not significant.
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
6.242424 - 8.000000
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
3.939394 - 7.454545
ai.central.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1809.11 27.47 65.867 < 2e-16 ***
ton.ord2 -178.42 29.78 -5.992 3.30e-09 ***
ton.ord3 -158.81 30.29 -5.243 2.09e-07 ***
ton.ord4 -152.09 28.53 -5.331 1.32e-07 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 1.106 1.204 6.368 0.01168 *
s(duree.ms.) 2.613 3.155 4.432 0.00315 **
ti(measurement.no,duree.ms.) 3.274 4.240 7.847 2.48e-06 ***
s(measurement.no):ton.ord2 1.001 1.002 1.433 0.23173
s(measurement.no):ton.ord3 1.001 1.002 0.023 0.88142
s(measurement.no):ton.ord4 1.002 1.003 0.342 0.56017 plot_smooth(ai.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.central.mas$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 80.084553 to 197.994270.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
ai.central.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1808.72 25.82 70.057 < 2e-16 ***
ton.ord2 -183.71 28.33 -6.485 1.74e-10 ***
ton.ord3 -198.13 29.76 -6.658 5.86e-11 ***
ton.ord4 -147.99 26.51 -5.582 3.48e-08 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 1.001 1.002 9.506 0.00212 **
s(f0) 4.105 4.579 7.115 2.97e-06 ***
ti(measurement.no,f0) 5.049 5.936 5.348 2.37e-05 ***
s(measurement.no):ton.ord2 1.001 1.001 0.421 0.51700
s(measurement.no):ton.ord3 1.001 1.001 0.465 0.49561
s(measurement.no):ton.ord4 1.001 1.001 0.610 0.43499 plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 100.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 120.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 140.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.mas.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.central.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; with 30 values ranging from 99.155553 to 204.798228.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
Féminin
F1
Basic models
We now switch to the data of feminin subjects.
ggplot(ai.fem, aes(x=measurement.no, y=f1, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()
The first model with a basic smooth of tone 1 and difference smooths.
ai.fem$ton.ord <- as.ordered(ai.fem$ton)
contrasts(ai.fem$ton.ord) <- "contr.treatment"
ai.fem.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="ML")
summary.coefs(ai.fem.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 727.300 10.502 69.251 < 2e-16 ***
ton.ord2 41.824 12.745 3.282 0.00106 **
ton.ord3 9.285 13.129 0.707 0.47957
ton.ord4 -4.129 11.138 -0.371 0.71090
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.990 6.014 22.139 < 2e-16 ***
s(measurement.no):ton.ord2 1.976 2.459 3.589 0.02225 *
s(measurement.no):ton.ord3 1.004 1.008 4.710 0.03005 *
s(measurement.no):ton.ord4 2.916 3.598 4.183 0.00373 ** Then the plots of predictions and difference smooth.
plot_smooth(ai.fem.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.fem.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 4.343434
plot_diff(ai.fem.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 2.323232
7.777778 - 10.000000
plot_diff(ai.fem.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 2.828283
6.161616 - 7.676768
The model that accounts for the influence of duree.ms. on the trajectories.
ai.fem.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="ML")
summary.coefs(ai.fem.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 725.48603 10.51394 69.002 < 2e-16 ***
ton.ord2 38.51010 12.39140 3.108 0.00193 **
ton.ord3 9.42465 13.15220 0.717 0.47377
ton.ord4 0.02272 11.37780 0.002 0.99841
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.275 6.330 25.533 < 2e-16 ***
s(duree.ms.) 7.132 8.041 6.159 < 2e-16 ***
ti(measurement.no,duree.ms.) 8.571 10.747 5.436 < 2e-16 ***
s(measurement.no):ton.ord2 2.039 2.534 3.642 0.01899 *
s(measurement.no):ton.ord3 1.007 1.012 10.009 0.00158 **
s(measurement.no):ton.ord4 2.434 3.015 2.159 0.09079 . The plots with regard the durations.
plot_smooth(ai.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.fem.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.fem$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 89.970028 to 186.666330.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
The model with regard of f0.
ai.fem.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="ML")
summary.coefs(ai.fem.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 726.023 10.603 68.474 <2e-16 ***
ton.ord2 36.953 14.222 2.598 0.0095 **
ton.ord3 15.256 14.126 1.080 0.2804
ton.ord4 -2.092 11.020 -0.190 0.8494
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.092 6.142 32.725 < 2e-16 ***
s(f0) 1.000 1.000 1.679 0.195397
ti(measurement.no,f0) 2.285 2.770 5.741 0.000985 ***
s(measurement.no):ton.ord2 1.000 1.001 0.466 0.495028
s(measurement.no):ton.ord3 1.000 1.001 0.245 0.620400
s(measurement.no):ton.ord4 2.564 3.184 3.425 0.015002 * The plot of such model.
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=220),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 220.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=240),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 240.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=260),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 260.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.fem.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 159.611858 to 275.883293.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
extra models
# random intercepts only
ai.fem.gam.int <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr") +
s(numero, bs="re"),
data=ai.fem, method="fREML")
summary.coefs(ai.fem.gam.int)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 748.285 11.493 65.107 <2e-16 ***
ton.ord2 35.242 14.163 2.488 0.013 *
ton.ord3 21.055 14.126 1.490 0.136
ton.ord4 1.546 11.010 0.140 0.888
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.1331 6.184 32.528 < 2e-16 ***
s(f0) 1.0000 1.000 1.098 0.29484
ti(measurement.no,f0) 3.5799 4.896 3.511 0.00416 **
s(measurement.no):ton.ord2 1.0001 1.000 0.150 0.69866
s(measurement.no):ton.ord3 1.0003 1.001 0.124 0.72537
s(measurement.no):ton.ord4 2.6626 3.295 3.625 0.01065 *
s(numero) 0.9609 1.000 24.572 7.72e-07 ***plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
fvisgam(ai.fem.gam.int, view=c("measurement.no","f0"),
ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 100.125696 to 212.400605.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero)
data length [31] is not a sub-multiple or multiple of the number of rows [30]
# random intercepts + slopes
ai.fem.gam.slope <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr") +
s(numero, bs="re") +
s(numero, measurement.no, bs="re"),
data=ai.fem, method="fREML")
summary.coefs(ai.fem.gam.slope)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 747.286 11.407 65.514 <2e-16 ***
ton.ord2 36.487 14.158 2.577 0.0101 *
ton.ord3 21.379 14.114 1.515 0.1301
ton.ord4 1.958 11.003 0.178 0.8588
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 5.1283 6.178 30.085 < 2e-16 ***
s(f0) 1.0001 1.000 1.323 0.25034
ti(measurement.no,f0) 3.4752 4.757 3.598 0.00400 **
s(measurement.no):ton.ord2 1.0002 1.000 0.156 0.69325
s(measurement.no):ton.ord3 1.0003 1.001 0.034 0.85413
s(measurement.no):ton.ord4 2.6666 3.300 3.757 0.00877 **
s(numero) 0.6759 1.000 5.927 0.07910 .
s(numero,measurement.no) 0.8148 1.000 12.630 0.02023 * plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
fvisgam(ai.fem.gam.slope, view=c("measurement.no","f0"),
ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 159.611858 to 275.883293.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
data length [31] is not a sub-multiple or multiple of the number of rows [30]
plot_smooth(ai.fem.gam.slope, view="measurement.no", plot_all="ton.ord",
rug=F, rm.ranef=T)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 203.561992329367.
* numero : numeric predictor; set to the value(s): 57. (Might be canceled as random effect, check below.)
* NOTE : The following random effects columns are canceled: s(numero),s(numero,measurement.no)
#plot_diff(ai.mas.gam.slope, view="measurement.no",
#comp=list(ton.ord = c("2","4")), rm.ranef=T)#ai.fem.gam.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
# s(f0, bs="cr") +
# ti(measurement.no, f0) +
# s(measurement.no, by=ton.ord, bs="cr") +
# s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
# data=ai.fem, method="fREML")
#summary.coefs(ai.fem.gam.smooth)ai.fem$start.event <- ai.fem$measurement.no==0
r1 <- start_value_rho(ai.fem.gam.f0)
ai.fem.gam.AR <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="fREML",
rho=r1, AR.start=ai.fem$start.event)
summary.coefs(ai.fem.gam.AR)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 730.847 17.036 42.901 <2e-16 ***
ton.ord2 44.139 22.273 1.982 0.0478 *
ton.ord3 8.903 22.447 0.397 0.6917
ton.ord4 -4.354 17.857 -0.244 0.8074
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 6.289 7.490 24.971 < 2e-16 ***
s(f0) 1.000 1.000 0.669 0.413682
ti(measurement.no,f0) 4.329 5.818 4.314 0.000372 ***
s(measurement.no):ton.ord2 1.000 1.000 0.805 0.369761
s(measurement.no):ton.ord3 1.000 1.001 0.385 0.535005
s(measurement.no):ton.ord4 3.102 3.972 3.223 0.013040 * plot_smooth(ai.fem.gam.AR, view="measurement.no", plot_all="ton.ord",
rug=F, rm.ranef=T)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 203.561992329367.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.fem.gam.AR, view="measurement.no",
comp=list(ton.ord = c("2","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 203.561992329367.
measurement.no window(s) of significant difference(s):
0.000000 - 3.939394
7.575758 - 10.000000
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=100),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 100.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=120),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 120.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=140),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 140.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=180),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=200),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.fem.gam.AR, view=c("measurement.no","f0"),
ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 159.611858 to 275.883293.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
Central portion
We now focus on the central portion of the diphthongs, which means the portions without the inluence of the consonant contexts.
ai.central.fem$ton.ord <- as.ordered(ai.central.fem$ton)
contrasts(ai.central.fem$ton.ord) <- "contr.treatment"
ai.central.fem.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 764.464 10.337 73.951 <2e-16 ***
ton.ord2 23.456 12.626 1.858 0.0636 .
ton.ord3 1.278 13.036 0.098 0.9219
ton.ord4 6.941 10.981 0.632 0.5275
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 2.787 3.350 9.759 1.85e-06 ***
s(measurement.no):ton.ord2 1.002 1.005 3.192 0.0744 .
s(measurement.no):ton.ord3 1.002 1.005 5.494 0.0192 *
s(measurement.no):ton.ord4 2.339 2.843 2.021 0.1193 plot_smooth(ai.central.fem.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.central.fem.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 4.848485
plot_diff(ai.central.fem.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
4.787879 - 7.090909
plot_diff(ai.central.fem.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 2.969697
5.151515 - 8.000000
ai.central.fem.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 765.093 10.573 72.362 <2e-16 ***
ton.ord2 24.013 12.492 1.922 0.055 .
ton.ord3 -2.690 13.290 -0.202 0.840
ton.ord4 6.656 11.439 0.582 0.561
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 2.800 3.363 10.238 9.86e-07 ***
s(duree.ms.) 4.244 4.713 3.477 0.0354 *
ti(measurement.no,duree.ms.) 2.227 2.802 2.612 0.0375 *
s(measurement.no):ton.ord2 1.001 1.002 3.054 0.0809 .
s(measurement.no):ton.ord3 1.001 1.002 5.522 0.0190 *
s(measurement.no):ton.ord4 2.354 2.860 2.097 0.1080 plot_smooth(ai.central.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.fem.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.central.fem$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 89.970028 to 186.666330.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
ai.central.fem.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 753.01 10.66 70.639 <2e-16 ***
ton.ord2 40.34 14.26 2.829 0.0048 **
ton.ord3 25.36 14.31 1.771 0.0769 .
ton.ord4 14.76 11.07 1.333 0.1828
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 2.956 3.535 12.622 < 2e-16 ***
s(f0) 1.000 1.001 9.090 0.00265 **
ti(measurement.no,f0) 1.003 1.006 7.019 0.00817 **
s(measurement.no):ton.ord2 1.001 1.002 1.025 0.31201
s(measurement.no):ton.ord3 1.001 1.001 1.560 0.21208
s(measurement.no):ton.ord4 2.117 2.590 1.603 0.18325 plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=220),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 220.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=240),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 240.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=260),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 260.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.fem.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.central.fem$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; with 30 values ranging from 159.333410 to 268.051028.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
F2
ggplot(ai.fem, aes(x=measurement.no, y=f2, group=numero,
alpha=duree.ms.)) +
facet_grid(~ton) + geom_line()
Basic models
We fit the same model with a basic smooth of tone 1 and difference smooths.
ai.fem.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="ML")
summary.coefs(ai.fem.f2.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2038.36 17.15 118.880 < 2e-16 ***
ton.ord2 -107.98 20.81 -5.189 2.49e-07 ***
ton.ord3 -88.98 21.43 -4.152 3.53e-05 ***
ton.ord4 -70.79 18.18 -3.893 0.000105 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.280 5.234 3.945 0.001255 **
s(measurement.no):ton.ord2 1.002 1.005 22.811 2.65e-06 ***
s(measurement.no):ton.ord3 2.149 2.673 9.920 9.98e-06 ***
s(measurement.no):ton.ord4 1.003 1.005 11.113 0.000866 ***Now the plots of f2 with different tones.
plot_smooth(ai.fem.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 6.666667
plot_diff(ai.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
4.545455 - 5.959596
plot_diff(ai.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 0.000000 to 10.000000.
measurement.no window(s) of significant difference(s):
0.000000 - 2.424242
The model that accounts for the influence of duree.ms. on the trajectories.
ai.fem.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(duree.ms., bs="cr") +
ti(measurement.no, duree.ms.) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="ML")
summary.coefs(ai.fem.f2.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2038.81 17.36 117.410 < 2e-16 ***
ton.ord2 -102.76 20.46 -5.023 5.88e-07 ***
ton.ord3 -87.08 21.69 -4.014 6.35e-05 ***
ton.ord4 -73.67 18.78 -3.922 9.29e-05 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 4.356 5.321 4.858 0.000157 ***
s(duree.ms.) 6.537 7.514 5.600 1.96e-06 ***
ti(measurement.no,duree.ms.) 3.189 3.659 7.475 1.13e-05 ***
s(measurement.no):ton.ord2 1.002 1.005 21.541 3.84e-06 ***
s(measurement.no):ton.ord3 2.182 2.713 8.207 6.17e-05 ***
s(measurement.no):ton.ord4 1.004 1.007 4.444 0.034836 * The plots with regard the durations.
plot_smooth(ai.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.fem.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.fem$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 89.970028 to 186.666330.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
ai.fem.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
s(f0, bs="cr") +
ti(measurement.no, f0) +
s(measurement.no, by=ton.ord, bs="cr"),
data=ai.fem, method="ML")
summary.coefs(ai.fem.f2.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2006.20 17.59 114.025 <2e-16 ***
ton.ord2 -47.56 23.69 -2.008 0.0449 *
ton.ord3 -39.15 23.59 -1.660 0.0972 .
ton.ord4 -33.95 18.66 -1.819 0.0691 .
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 3.765 4.647 3.339 0.006051 **
s(f0) 4.465 5.325 9.702 < 2e-16 ***
ti(measurement.no,f0) 1.001 1.002 3.953 0.046912 *
s(measurement.no):ton.ord2 1.000 1.001 7.075 0.007921 **
s(measurement.no):ton.ord3 1.878 2.345 4.999 0.004516 **
s(measurement.no):ton.ord4 1.001 1.002 14.369 0.000158 ***plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=220),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 220.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=240),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 240.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=260),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; set to the value(s): 260.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.fem.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 0.000000 to 10.000000.
* f0 : numeric predictor; with 30 values ranging from 159.611858 to 275.883293.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
Central portion
The central portion:
ai.central.fem.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.f2.gam.diff)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2048.03 17.96 114.046 < 2e-16 ***
ton.ord2 -110.36 21.93 -5.032 6.06e-07 ***
ton.ord3 -68.99 22.65 -3.047 0.00239 **
ton.ord4 -74.75 19.08 -3.919 9.70e-05 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 1.354 1.610 3.247 0.03218 *
s(measurement.no):ton.ord2 1.002 1.003 7.512 0.00623 **
s(measurement.no):ton.ord3 1.002 1.004 6.353 0.01188 *
s(measurement.no):ton.ord4 1.108 1.194 1.388 0.20091 plot_smooth(ai.central.fem.f2.gam.diff, view="measurement.no",
plot_all="ton.ord", rug=F)Summary:
* ton.ord : factor; set to the value(s): 1, 2, 3, 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* NOTE : No random effects in the model to cancel.
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("1","2")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 6.727273
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","3")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
3.757576 - 5.818182
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("3","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
Difference is not significant.
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
comp=list(ton.ord=c("2","4")))Summary:
* measurement.no : numeric predictor; with 100 values ranging from 2.000000 to 8.000000.
measurement.no window(s) of significant difference(s):
2.000000 - 5.333333
ai.central.fem.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(duree.ms., bs="cr",k=6) +
ti(measurement.no, duree.ms.,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.f2.gam.dur)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2057.82 18.18 113.168 < 2e-16 ***
ton.ord2 -114.28 21.48 -5.321 1.36e-07 ***
ton.ord3 -74.34 22.87 -3.251 0.0012 **
ton.ord4 -88.36 19.67 -4.491 8.18e-06 ***
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 1.226 1.395 6.156 0.005088 **
s(duree.ms.) 4.351 4.781 5.615 0.000994 ***
ti(measurement.no,duree.ms.) 2.317 2.905 5.698 0.001249 **
s(measurement.no):ton.ord2 1.001 1.002 7.310 0.006986 **
s(measurement.no):ton.ord3 1.001 1.002 4.581 0.032602 *
s(measurement.no):ton.ord4 1.276 1.479 0.171 0.687964 plot_smooth(ai.central.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 170.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; set to the value(s): 80.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.fem.f2.gam.dur, view=c("measurement.no","duree.ms."),
ylim=quantile(ai.central.fem$duree.ms., c(0.1, 0.9)))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* duree.ms. : numeric predictor; with 30 values ranging from 89.970028 to 186.666330.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
ai.central.fem.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
s(f0, bs="cr",k=6) +
ti(measurement.no, f0,k=6) +
s(measurement.no, by=ton.ord, bs="cr",k=6),
data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.f2.gam.f0)Parametric coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2010.59 18.67 107.680 <2e-16 ***
ton.ord2 -46.55 25.44 -1.830 0.0677 .
ton.ord3 -24.49 25.01 -0.979 0.3278
ton.ord4 -31.78 19.74 -1.610 0.1079
Approximate significance of smooth terms:
edf Ref.df F p-value
s(measurement.no) 1.019 1.036 9.108 0.00263 **
s(f0) 3.656 4.215 12.066 < 2e-16 ***
ti(measurement.no,f0) 2.960 3.539 4.413 0.00284 **
s(measurement.no):ton.ord2 1.001 1.001 2.724 0.09917 .
s(measurement.no):ton.ord3 1.001 1.001 2.309 0.12896
s(measurement.no):ton.ord4 1.158 1.296 1.005 0.41341 plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=180),
rug=F, col="red")Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 180.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=200),
rug=F, col="orange", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 200.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=220),
rug=F, col="yellow", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 220.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=240),
rug=F, col="green", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 240.
* NOTE : No random effects in the model to cancel.
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=260),
rug=F, col="blue", add=T)Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; set to the value(s): 260.
* NOTE : No random effects in the model to cancel.
fvisgam(ai.central.fem.f2.gam.f0, view=c("measurement.no","f0"),
ylim=quantile(ai.central.fem$f0, c(0.1, 0.9), na.rm=TRUE))Summary:
* ton.ord : factor; set to the value(s): 4.
* measurement.no : numeric predictor; with 30 values ranging from 2.000000 to 8.000000.
* f0 : numeric predictor; with 30 values ranging from 159.333410 to 268.051028.
* NOTE : No random effects in the model to cancel.
data length [31] is not a sub-multiple or multiple of the number of rows [30]
Predictions and vowel space
---
title: 'Notebook: Modélisation GAMMs de la diphtongue descendante /ai/ en mandarin
  standard'
author: "Chenyu LI"
output:
  html_document:
    df_print: paged
---

Ce notebook est une analyse acoustique des **diphtongues descendantes** en **mandarin standard**, en appliquant la modélisation **GAMMs** (Generated Additive Mixed Models).

```{r}
#install.packages('rmarkdown')
```

```{r}
# Importation des emballages 
#install.packages("itsadug")
library(nlme)
library(ggplot2)
library(mgcv)
library(itsadug)
source("gamm_hacks.r")
#install.packages("tidyverse")
library(tidyverse)
```

Après l'importation des emballages, nous lisons les bases de données en format *.tsv*.

```{r}
# Importation des données 

au <- read.table(file="au0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ai <- read.table(file="ai0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ei <- read.table(file="ei0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
ou <- read.table(file="ou0a.tsv",sep="\t", fileEncoding = 'UTF-16', header = TRUE)
```

**Changement de nature des variables des bases de données.**

Le critère du changement est que toutes les variables numériques sont numérées et toutes les chaînes de caractère sont factorées.

# /ai/

La première diphtongue à analyser est **/ai/**.

```{r}
ai$sexe<-as.factor(ai$sexe)
ai$ton<-as.factor(ai$ton)
ai$pow<-as.factor(ai$pow)
ai$contexte.D<-as.factor(ai$contexte.D)
ai$contexte.G<-as.factor(ai$contexte.G)

ai$f1<-as.numeric(ai$f1)
ai$f2<-as.numeric(ai$f2)
ai$f3<-as.numeric(ai$f3)
ai$f0<-as.numeric(ai$f0)
```

Nous voyons la tête du fichier de /ai/.

```{r}
head(ai)
```
Dans la base de données, nous pouvons remarquer les variables basiques dans les colonnes *numero*, *sexe*, *locuteur*, *diphtongue* ; les variables catégorielles comme *ton*, *pow* (position dans le mot), *contexte.G* et *contexte.D* (les consonnes précédentes et suivantes) ; et les variables numériques comme *duree.ms*, *f0*, *f1*, *f2* et *f3*. Chaque diphtongue est mesurée par 11 points temporels à intervalle égale (0%, 10%, 20%, ..., 100%).


```{r}
# Regroupement par les sexes
ai.mas <- droplevels(subset(ai,sexe=="M"))
ai.fem <- droplevels(subset(ai,sexe=="F"))
```

## Masculin

Nous allons voir les données des locuteurs.

### F1

Avant de lancer les modèles, nous voyons les **trajectoires de f1** avec les tons différents, avec la considération des sexes et de la durée.

```{r}
ggplot(ai.mas, aes(x=measurement.no, y=f1, group=numero,
                     alpha=duree.ms.)) +
  facet_grid(~ton) + geom_line()
```
#### Modèles de base

Le premier modèle est avec un smooth basic de *ton 1* et de *smooths de différence*.


```{r}
ai.mas$ton.ord <- as.ordered(ai.mas$ton)
contrasts(ai.mas$ton.ord) <- "contr.treatment"
ai.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                        s(measurement.no, by=ton.ord, bs="cr"),
                      data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.diff)
```

Ensuite les plots des *prédictions* et de *smooth de différence* entre les tons. 


```{r}
plot_smooth(ai.mas.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.mas.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","4")))
plot_diff(ai.mas.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.mas.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
```

**Analyse :** Selon les images, les trajactoires de f1 dans les 4 tons peuvent généralement être différentes significativement, à l'excepetion de celles des ton 3 et 4, qui ne sont pas différentes significativement dans la portion centrale.

Maintenant nous tenons compte de l'influence de *duree.ms* sur les trajactoires. 

```{r}
ai.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(duree.ms., bs="cr") +
                              ti(measurement.no, duree.ms.) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.dur)
```

Les plots avec la considération de la *durée*.

```{r}
plot_smooth(ai.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.mas$duree.ms., c(0.1, 0.9)))
```

The model with regard of *f0*.

```{r}
ai.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(f0, bs="cr") +
                              ti(measurement.no, f0) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.mas, method="ML")
summary.coefs(ai.mas.gam.f0)
```

The plot of such model.

```{r}
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```

#### extra models

```{r}
 # random intercepts only
ai.mas.gam.int <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                             s(f0, bs="cr") +
                             ti(measurement.no, f0) +
                             s(measurement.no, by=ton.ord, bs="cr") +
                             s(numero, bs="re"),
                         data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.int)
```

```{r}
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.gam.int, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.int, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```


```{r}
# random intercepts + slopes
ai.mas.gam.slope <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                            s(f0, bs="cr") +
                            ti(measurement.no, f0) +
                            s(measurement.no, by=ton.ord, bs="cr") +
                            s(numero, bs="re") +
                            s(numero, measurement.no, bs="re"),
                        data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.slope)
```

```{r}
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.gam.slope, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.slope, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```

```{r}
plot_smooth(ai.mas.gam.slope, view="measurement.no", plot_all="ton.ord",
            rug=F, rm.ranef=T)
#plot_diff(ai.mas.gam.slope, view="measurement.no",
          #comp=list(ton.ord = c("2","4")), rm.ranef=T)
```


```{r}
#ai.mas.gam.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
#                           s(f0, bs="cr") +
#                           ti(measurement.no, f0) +
#                           s(measurement.no, by=ton.ord, bs="cr") +
#                           s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
#                         data=ai.mas, method="fREML")
#summary.coefs(ai.mas.gam.smooth)
```


```{r}
ai.mas$start.event <- ai.mas$measurement.no==0

r1 <- start_value_rho(ai.mas.gam.f0)

ai.mas.gam.AR <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                            s(f0, bs="cr") +
                            ti(measurement.no, f0) +
                            s(measurement.no, by=ton.ord, bs="cr"),
                       data=ai.mas, method="fREML",
                       rho=r1, AR.start=ai.mas$start.event)
summary.coefs(ai.mas.gam.AR)
```

```{r}
plot_smooth(ai.mas.gam.AR, view="measurement.no", plot_all="ton.ord",
            rug=F, rm.ranef=T)
plot_diff(ai.mas.gam.AR, view="measurement.no",
          comp=list(ton.ord = c("2","4")))
```

```{r}
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.gam.AR, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.AR, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```


#### Central portion

We now focus on the *central portion* of the diphthongs, which means the portions without the inluence of the consonant contexts.

```{r}
ai.central<-droplevels(subset(ai,measurement.no>=2))
ai.central<-droplevels(subset(ai.central,measurement.no<=8))

ai.central.mas <- droplevels(subset(ai.central,sexe=="M"))
ai.central.fem <- droplevels(subset(ai.central,sexe=="F"))
```

```{r}
ai.central.mas$ton.ord <- as.ordered(ai.central.mas$ton)
contrasts(ai.central.mas$ton.ord) <- "contr.treatment"
ai.central.mas.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                        s(measurement.no, by=ton.ord, bs="cr",k=6),
                      data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.diff)
```

```{r}
plot_smooth(ai.central.mas.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.central.mas.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
```

```{r}
ai.central.mas.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(duree.ms., bs="cr",k=6) +
                              ti(measurement.no, duree.ms.,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.dur)
```

```{r}
plot_smooth(ai.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.central.mas.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.central.mas.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.central.mas$duree.ms., c(0.1, 0.9)))
```

```{r}
ai.central.mas.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(f0, bs="cr",k=6) +
                              ti(measurement.no, f0,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.gam.f0)
```

```{r}
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.central.mas.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.central.mas.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.central.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```

### F2

#### Basic models

Now we look at the *trajectories of f2* in different tones with regard of the sexes and the durations.

```{r}
ggplot(ai.mas, aes(x=measurement.no, y=f2, group=numero,
                     alpha=duree.ms.)) +
  facet_grid(~ton) + geom_line()
```

Then we fit the same model with a basic smooth of *tone 1* and *difference smooths*.

```{r}
ai.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                        s(measurement.no, by=ton.ord, bs="cr"),
                      data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.diff)
```

Now the plots of *f2* with different *tones*.

```{r}
plot_smooth(ai.mas.f2.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
```

The model that accounts for the influence of *duree.ms.* on the trajectories.

```{r}
ai.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(duree.ms., bs="cr") +
                              ti(measurement.no, duree.ms.) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.dur)
```

The plots with regard the *durations*.

```{r}
plot_smooth(ai.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.mas$duree.ms., c(0.1, 0.9)))
```

```{r}
ai.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(f0, bs="cr") +
                              ti(measurement.no, f0) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.mas, method="ML")
summary.coefs(ai.mas.f2.gam.f0)
```

```{r}
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.f2.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```


#### extra models

```{r}
 # random intercepts only
ai.mas.gam.f2.int <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                             s(f0, bs="cr") +
                             ti(measurement.no, f0) +
                             s(measurement.no, by=ton.ord, bs="cr") +
                             s(numero, bs="re"),
                         data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.f2.int)
```

```{r}
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.gam.f2.int, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.f2.int, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```


```{r}
# random intercepts + slopes
ai.mas.gam.f2.slope <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                            s(f0, bs="cr") +
                            ti(measurement.no, f0) +
                            s(measurement.no, by=ton.ord, bs="cr") +
                            s(numero, bs="re") +
                            s(numero, measurement.no, bs="re"),
                        data=ai.mas, method="fREML")
summary.coefs(ai.mas.gam.f2.slope)
```

```{r}
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.mas.gam.f2.slope, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```

```{r}
plot_smooth(ai.mas.gam.f2.slope, view="measurement.no", plot_all="ton.ord",
            rug=F, rm.ranef=T)
#plot_diff(ai.mas.gam.slope, view="measurement.no",
          #comp=list(ton.ord = c("2","4")), rm.ranef=T)
```


```{r}
#ai.mas.gam.f2.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
#                           s(f0, bs="cr") +
#                           ti(measurement.no, f0) +
#                           s(measurement.no, by=ton.ord, bs="cr") +
#                           s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
#                         data=ai.mas, method="fREML")
#summary.coefs(ai.mas.gam.f2.smooth)
```


#### Central portion

The central portion:

```{r}
ai.central.mas.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                        s(measurement.no, by=ton.ord, bs="cr",k=6),
                      data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.diff)
```

```{r}
plot_smooth(ai.central.mas.f2.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
plot_diff(ai.central.mas.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","4")))
```

```{r}
ai.central.mas.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(duree.ms., bs="cr",k=6) +
                              ti(measurement.no, duree.ms.,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.dur)
```

```{r}
plot_smooth(ai.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.central.mas.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.central.mas.f2.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.central.mas$duree.ms., c(0.1, 0.9)))
```

```{r}
ai.central.mas.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(f0, bs="cr",k=6) +
                              ti(measurement.no, f0,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.mas, method="ML")
summary.coefs(ai.central.mas.f2.gam.f0)
```

```{r}
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.central.mas.f2.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.central.mas.f2.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.central.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```



## Féminin

### F1

#### Basic models

We now switch to the data of **feminin** subjects.


```{r}
ggplot(ai.fem, aes(x=measurement.no, y=f1, group=numero,
                     alpha=duree.ms.)) +
  facet_grid(~ton) + geom_line()
```


The first model with a basic smooth of *tone 1* and *difference smooths*.

```{r}
ai.fem$ton.ord <- as.ordered(ai.fem$ton)
contrasts(ai.fem$ton.ord) <- "contr.treatment"
ai.fem.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                        s(measurement.no, by=ton.ord, bs="cr"),
                      data=ai.fem, method="ML")
summary.coefs(ai.fem.gam.diff)
```

Then the plots of *predictions* and *difference smooth*.

```{r}
plot_smooth(ai.fem.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.fem.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.fem.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.fem.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
```

The model that accounts for the influence of *duree.ms.* on the trajectories.

```{r}
ai.fem.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(duree.ms., bs="cr") +
                              ti(measurement.no, duree.ms.) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.fem, method="ML")
summary.coefs(ai.fem.gam.dur)
```

The plots with regard the *durations*.

```{r}
plot_smooth(ai.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.fem$duree.ms., c(0.1, 0.9)))
```

The model with regard of *f0*.

```{r}
ai.fem.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(f0, bs="cr") +
                              ti(measurement.no, f0) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.fem, method="ML")
summary.coefs(ai.fem.gam.f0)
```

The plot of such model.

```{r}
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="red")
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="orange", add=T)
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=220),
            rug=F, col="yellow", add=T)
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=240),
            rug=F, col="green", add=T)
plot_smooth(ai.fem.gam.f0, view="measurement.no", cond=list(f0=260),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))
```


#### extra models

```{r}
 # random intercepts only
ai.fem.gam.int <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                             s(f0, bs="cr") +
                             ti(measurement.no, f0) +
                             s(measurement.no, by=ton.ord, bs="cr") +
                             s(numero, bs="re"),
                         data=ai.fem, method="fREML")
summary.coefs(ai.fem.gam.int)
```

```{r}
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.fem.gam.int, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.gam.int, view=c("measurement.no","f0"),
        ylim=quantile(ai.mas$f0, c(0.1, 0.9), na.rm=TRUE))
```


```{r}
# random intercepts + slopes
ai.fem.gam.slope <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                            s(f0, bs="cr") +
                            ti(measurement.no, f0) +
                            s(measurement.no, by=ton.ord, bs="cr") +
                            s(numero, bs="re") +
                            s(numero, measurement.no, bs="re"),
                        data=ai.fem, method="fREML")
summary.coefs(ai.fem.gam.slope)
```

```{r}
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.fem.gam.slope, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.gam.slope, view=c("measurement.no","f0"),
        ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))
```

```{r}
plot_smooth(ai.fem.gam.slope, view="measurement.no", plot_all="ton.ord",
            rug=F, rm.ranef=T)
#plot_diff(ai.mas.gam.slope, view="measurement.no",
          #comp=list(ton.ord = c("2","4")), rm.ranef=T)
```


```{r}
#ai.fem.gam.smooth <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
#                           s(f0, bs="cr") +
#                           ti(measurement.no, f0) +
#                           s(measurement.no, by=ton.ord, bs="cr") +
#                           s(measurement.no, numero, bs="fs", xt="cr", m=1, k=5),
#                         data=ai.fem, method="fREML")
#summary.coefs(ai.fem.gam.smooth)
```

```{r}
ai.fem$start.event <- ai.fem$measurement.no==0

r1 <- start_value_rho(ai.fem.gam.f0)

ai.fem.gam.AR <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr") +
                            s(f0, bs="cr") +
                            ti(measurement.no, f0) +
                            s(measurement.no, by=ton.ord, bs="cr"),
                       data=ai.fem, method="fREML",
                       rho=r1, AR.start=ai.fem$start.event)
summary.coefs(ai.fem.gam.AR)
```

```{r}
plot_smooth(ai.fem.gam.AR, view="measurement.no", plot_all="ton.ord",
            rug=F, rm.ranef=T)
plot_diff(ai.fem.gam.AR, view="measurement.no",
          comp=list(ton.ord = c("2","4")))
```

```{r}
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=100),
            rug=F, col="red")
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=120),
            rug=F, col="orange", add=T)
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=140),
            rug=F, col="yellow", add=T)
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=180),
            rug=F, col="green", add=T)
plot_smooth(ai.fem.gam.AR, view="measurement.no", cond=list(f0=200),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.gam.AR, view=c("measurement.no","f0"),
        ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))
```





#### Central portion

We now focus on the *central portion* of the diphthongs, which means the portions without the inluence of the consonant contexts.


```{r}
ai.central.fem$ton.ord <- as.ordered(ai.central.fem$ton)
contrasts(ai.central.fem$ton.ord) <- "contr.treatment"
ai.central.fem.gam.diff <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                        s(measurement.no, by=ton.ord, bs="cr",k=6),
                      data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.gam.diff)
```

```{r}
plot_smooth(ai.central.fem.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.central.fem.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.central.fem.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.central.fem.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
```

```{r}
ai.central.fem.gam.dur <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(duree.ms., bs="cr",k=6) +
                              ti(measurement.no, duree.ms.,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.gam.dur)
```

```{r}
plot_smooth(ai.central.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.central.fem.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.central.fem.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.central.fem$duree.ms., c(0.1, 0.9)))
```

```{r}
ai.central.fem.gam.f0 <- bam(f1 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(f0, bs="cr",k=6) +
                              ti(measurement.no, f0,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.gam.f0)
```

```{r}
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="red")
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="orange", add=T)
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=220),
            rug=F, col="yellow", add=T)
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=240),
            rug=F, col="green", add=T)
plot_smooth(ai.central.fem.gam.f0, view="measurement.no", cond=list(f0=260),
            rug=F, col="blue", add=T)
fvisgam(ai.central.fem.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.central.fem$f0, c(0.1, 0.9), na.rm=TRUE))
```


### F2

```{r}
ggplot(ai.fem, aes(x=measurement.no, y=f2, group=numero,
                     alpha=duree.ms.)) +
  facet_grid(~ton) + geom_line()
```


#### Basic models

We fit the same model with a basic smooth of *tone 1* and *difference smooths*.

```{r}
ai.fem.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                        s(measurement.no, by=ton.ord, bs="cr"),
                      data=ai.fem, method="ML")
summary.coefs(ai.fem.f2.gam.diff)
```

Now the plots of *f2* with different *tones*.

```{r}
plot_smooth(ai.fem.f2.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
```

The model that accounts for the influence of *duree.ms.* on the trajectories.

```{r}
ai.fem.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(duree.ms., bs="cr") +
                              ti(measurement.no, duree.ms.) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.fem, method="ML")
summary.coefs(ai.fem.f2.gam.dur)
```

The plots with regard the *durations*.

```{r}
plot_smooth(ai.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.f2.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.fem$duree.ms., c(0.1, 0.9)))
```

```{r}
ai.fem.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr") +
                              s(f0, bs="cr") +
                              ti(measurement.no, f0) +
                              s(measurement.no, by=ton.ord, bs="cr"),
                         data=ai.fem, method="ML")
summary.coefs(ai.fem.f2.gam.f0)
```

```{r}
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="red")
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="orange", add=T)
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=220),
            rug=F, col="yellow", add=T)
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=240),
            rug=F, col="green", add=T)
plot_smooth(ai.fem.f2.gam.f0, view="measurement.no", cond=list(f0=260),
            rug=F, col="blue", add=T)
fvisgam(ai.fem.f2.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.fem$f0, c(0.1, 0.9), na.rm=TRUE))
```

#### Central portion

The central portion:

```{r}
ai.central.fem.f2.gam.diff <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                        s(measurement.no, by=ton.ord, bs="cr",k=6),
                      data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.f2.gam.diff)
```

```{r}
plot_smooth(ai.central.fem.f2.gam.diff, view="measurement.no",
            plot_all="ton.ord", rug=F)
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("1","2")))
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","3")))
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("3","4")))
plot_diff(ai.central.fem.f2.gam.diff, view="measurement.no",
          comp=list(ton.ord=c("2","4")))
```

```{r}
ai.central.fem.f2.gam.dur <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(duree.ms., bs="cr",k=6) +
                              ti(measurement.no, duree.ms.,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.f2.gam.dur)
```

```{r}
plot_smooth(ai.central.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=170),
            rug=F, col="red")
plot_smooth(ai.central.fem.f2.gam.dur, view="measurement.no", cond=list(duree.ms.=80),
            rug=F, col="blue", add=T)
fvisgam(ai.central.fem.f2.gam.dur, view=c("measurement.no","duree.ms."),
        ylim=quantile(ai.central.fem$duree.ms., c(0.1, 0.9)))
```

```{r}
ai.central.fem.f2.gam.f0 <- bam(f2 ~ ton.ord + s(measurement.no, bs="cr",k=6) +
                              s(f0, bs="cr",k=6) +
                              ti(measurement.no, f0,k=6) +
                              s(measurement.no, by=ton.ord, bs="cr",k=6),
                         data=ai.central.fem, method="ML")
summary.coefs(ai.central.fem.f2.gam.f0)
```

```{r}
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=180),
            rug=F, col="red")
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=200),
            rug=F, col="orange", add=T)
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=220),
            rug=F, col="yellow", add=T)
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=240),
            rug=F, col="green", add=T)
plot_smooth(ai.central.fem.f2.gam.f0, view="measurement.no", cond=list(f0=260),
            rug=F, col="blue", add=T)
fvisgam(ai.central.fem.f2.gam.f0, view=c("measurement.no","f0"),
        ylim=quantile(ai.central.fem$f0, c(0.1, 0.9), na.rm=TRUE))
```

## Predictions and vowel space 







